def mttkrp(self, U, n):
# Compute the matrix product of the n-mode matricization of self with the KR
# product of all entries in U, a list of matrices, except the nth one.
X = self.data
N = X.ndim
order = range(N)
order_sans_n = order[:]; order_sans_n.remove(n)
Xn = X.transpose([n] + order_sans_n)
Xn = Xn.reshape(X.shape[n], X.size / X.shape[n])
Z = tt.khatrirao([U[i] for i in order_sans_n], reverse=True)
return np.dot(Xn, Z)
def mttkrp(self, U, n):
# Compute the matrix product of the n-mode matricization of self with the KR
# product of all entries in U, a list of matrices, except the nth one.
X = self.data
N = X.ndim
order = range(N)
order_sans_n = order[:]
order_sans_n.remove(n)
Xn = X.transpose([n] + order_sans_n)
Xn = Xn.reshape(X.shape[n], X.size / X.shape[n])
Z = tt.khatrirao([U[i] for i in order_sans_n], reverse=True)
return np.dot(Xn, Z)
def full(self):
# Convert to a full dense tensor.
A = tt.khatrirao(self.u_list, reverse=True)
data = np.dot(self.lmbda.T, A.T)
return tensor.Tensor(data, self.shape())
